中圖分類號： TN02,；TP212 文獻(xiàn)標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.211309 中文引用格式： 王潔，陶洋,，梁志芳. 基于改進(jìn)型極限學(xué)習(xí)機(jī)的電子鼻氣體濃度檢測[J].電子技術(shù)應(yīng)用,，2021，47(10)：63-67. 英文引用格式： Wang Jie,，Tao Yang,，Liang Zhifang. Gas concentration detection of E-nose based on improved ELM[J]. Application of Electronic Technique，2021,，47(10)：63-67.
Gas concentration detection of E-nose based on improved ELM
Wang Jie,，Tao Yang，Liang Zhifang
School of Communication and Information Engineering,，Chongqing University of Posts and Telecommunications,， Chongqing 400065，China
Abstract： Aiming at unsatisfied ideal accuracy of electronic nose while testing the concentration of gas pollutants,，the particle swarm optimization and artificial bee colony algorithm based extreme learning machine(PSOABC-ELM) algorithm is proposed. The accuracy of electronic nose concentration detection is enhanced by improve extreme learning machine weights of input layer and hidden layer and hidden layer threshold random defects. PSOABC-ELM is compared with other algorithms and validated on the public data set. The results show that the PSOABC-ELM algorithm perform better than the others when Testinggas concentration of electronic nose, and the detection result error is smaller and the algorithm stability is stronger, which provides a new method for the detection of gas concentration of electronic nose.
